Console and script panes in RStudio

We’re going to make this today!

Everything in R is an “object”

Variable: a symbol that stores/represents some other value or set of values.

Think of variables as containers.

Variables get assigned their value in R with a left arrow <-

• Numeric variables represent numbers

x <- 5

• This means the value of the variable we have named x is equal to 5.

• String variables represent “strings” of text. Text has to be enclosed in quotes ""

my_var <- “hello”

• This means the value of the variable my_var is equal to the text “hello”.

General rules:

Numeric variables

In your console, do the following:

• Create a variable x and assign it a value of some number
• Create a variable of y and assign it a value of some other number
• Create some other variable (name it whatever you like!) and assign it a value of yet another number

String variables

In your console, do the following:

• Create a variable a and assign it a value of some text (don’t forget to enclose the text in quotes!)
• Create a variable b and assign it a value of some other text
• Create a variable with another name and assign it yet another text value

1. Numeric

• Integers (whole numbers)

1. Strings: collection of characters

• e.g., “abc”, “the rainbow is a division of white light”
• numbers and other non-letter characters can also be treated as strings

1. Factors: categorical variables. Make up a finite set

• e.g., “Blue” “Green” “Red”

1. Logicals: special kind of factor that only has two values

• TRUE vs FALSE, 0 vs 1

1. Data frames: collections of vectors
2. Vectors: collection of similar elements (numbers, characters, factors, etc..)
3. Matrices
4. Lists
5. Arrays

• In R code, any line of code preceded by a hashmark (#) is not evaluated (i.e., it’s ignored)
• You can write notes to your future self this way

# Set x equal to 0
x <- 0
# Now add 1 to the value of x
y <- x+1

Packages are bundles of code written to do (typically) specific sets of functions

• Some packages are automatically downloaded and loaded into your workspace when you install R
• Others you have to explicitly download
• Many packages are hosted on CRAN - this is the official “home” of peer-approved packages
  • These can be installed using the function install.packages().

install.packages("tidyverse")

• Other packages are not hosted on CRAN - many of these are excellent, but some may be less reliable.
  • Many of these are hosted on GitHub.com
  • These usually have to be installed using a function install_github() which is part of the devtools package.

# install.packages("devtools")
devtools::install_github("hadley/emo")

For example: To install the emo package from Hadley Wickham’s Github page:

• https://github.com/hadley/emo

• Libraries in R refer to where the packages are stored. When you install a new package, it gets automatically saved to a particular location (you don’t need to specify where). When you want to use the contents/functions of a package, you need to “load the library” using the library() function.

# load the tidyverse package
# expect a bunch of output messages (this is normal)
library(tidyverse) 

## ── Attaching packages ───────────────────────────────────────────────────────────────────────────────────────────────────────────────── tidyverse 1.2.1 ──

## ✓ ggplot2 3.2.1     ✓ purrr   0.3.3
## ✓ tibble  2.1.3     ✓ dplyr   0.8.3
## ✓ tidyr   0.8.3     ✓ stringr 1.4.0
## ✓ readr   1.3.1     ✓ forcats 0.4.0

## ── Conflicts ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()

• If you ask R to load a package you don’t have installed, or make a typo in the package name, R will yell at you (give you an error):

# Typo! what happens? R yells at you.
library(tidverse)

## Error in library(tidverse): there is no package called 'tidverse'

Functions: A certain named format of code that outlines a procedure. Often this allows several lines of code to be executed with a single line of code (by using the name of the function)

• In other words, functions are actions.
• Most functions take arguments
  • What do you want to act on?

“The tidyverse is an opinionated collection of R packages designed for data science. All packages share an underlying design philosophy, grammar, and data structures.” www.tidyverse.org

• Changed the game of R coding
• Much more intuitive syntax (IMHO); more “english-like”
• BUT not everyone likes it. Sometimes makes things easier, sometimes makes things more complicated.
• We’ll be using both
• Core packages: ggplot2, dplyr, tidyr, readr, stringr…

• We will cover both base R and the Tidyverse

Open 1_prep_data.R from today’s materials.

The goal of today’s workshop is to establish a workflow for using the rmarkdown package to “knit” together R code and text to create summary documents.

It might seem kind of odd to launch into a workflow that ties together R code and other stuff before we’ve actually had a chance to learn any R code, but here’s the rationale:

1. 🛠 Foundational skill: R Markdown document creations can serve as a way to document the rest of our skills. At each workshop, we’ll create another R Markdown document (extension .Rmd) to log the skills we worked on and any notes you’d like to keep for yourself. In this way, this skill is foundational.
2. 🙌 Instant gratification! R Markdown documents can be rendered without much working knowledge of R at all. It’s a lovely thing when you can get something up and running RIGHT AWAY

• inline R code

• Google your errors!
• Google “how to use XX package in r”
• Search Twitter (#rstats)